Accurate and robust segmentation of abdominal organs on CT is essential for
many clinical applications such as computer-aided diagnosis and computer-aided
surgery. But this task is challenging due to the weak boundaries of organs, the
complexity of the background, and the variable sizes of different organs. To
address these challenges, we introduce a novel framework for multi-organ
segmentation by using organ-attention networks with reverse connections
(OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output
estimates which are combined by statistical fusion exploiting structural
similarity. OAN is a two-stage deep convolutional network, where deep network
features from the first stage are combined with the original image, in a second
stage, to reduce the complex background and enhance the discriminative
information for the target organs. RCs are added to the first stage to give the
lower layers semantic information thereby enabling them to adapt to the sizes
of different organs. Our networks are trained on 2D views enabling us to use
holistic information and allowing efficient computation. To compensate for the
limited cross-sectional information of the original 3D volumetric CT,
multi-sectional images are reconstructed from the three different 2D view
directions. Then we combine the segmentation results from the different views
using statistical fusion, with a novel term relating the structural similarity
of the 2D views to the original 3D structure. To train the network and evaluate
results, 13 structures were manually annotated by four human raters and
confirmed by a senior expert on 236 normal cases. We tested our algorithm and
computed Dice-Sorensen similarity coefficients and surface distances for
evaluating our estimates of the 13 structures. Our experiments show that the
proposed approach outperforms 2D- and 3D-patch based state-of-the-art methods.

使用器官注意力网络，结合反向连接，以 2D 视图特征分割 3D CT 体积中的腹部器官。统计融合不同方向上的 2D 视图，实现准确和鲁棒的分割结果。

采用器官注意力网络和统计融合的腹部多器官分割

Abdominal multi-organ segmentation with organ-attention networks and  statistical fusion

Deep convolutional neural networks (CNNs), especially fully convolutional
networks, have been widely applied to automatic medical image segmentation
problems, e.g., multi-organ segmentation. Existing CNN-based segmentation
methods mainly focus on looking for increasingly powerful network
architectures, but pay less attention to data sampling strategies for training
networks more effectively. In this paper, we present a simple but effective
sample selection method for training multi-organ segmentation networks. Sample
selection exhibits an exploitation-exploration strategy, i.e., exploiting hard
samples and exploring less frequently visited samples. Based on the fact that
very hard samples might have annotation errors, we propose a new sample
selection policy, named Relaxed Upper Confident Bound (RUCB). Compared with
other sample selection policies, e.g., Upper Confident Bound (UCB), it exploits
a range of hard samples rather than being stuck with a small set of very hard
ones, which mitigates the influence of annotation errors during training. We
apply this new sample selection policy to training a multi-organ segmentation
network on a dataset containing 120 abdominal CT scans and show that it boosts
segmentation performance significantly.

本文利用一种新的样本选择策略，名为松弛上界置信度（RUCB），以强化与探索策略训练深度卷积神经网络，提高医学图像多器官分割的准确性。该方法有效地解决了注释错误对训练的影响，且在 120 个腹部 CT 扫描数据集上获得了显著的表现提升。

利用松弛上置信区间进行样本选择的多器官分割网络训练

Training Multi-organ Segmentation Networks with Sample Selection by  Relaxed Upper Confident Bound

In multi-organ segmentation of abdominal CT scans, most existing fully
supervised deep learning algorithms require lots of voxel-wise annotations,
which are usually difficult, expensive, and slow to obtain. In comparison,
massive unlabeled 3D CT volumes are usually easily accessible. Current
mainstream works to address the semi-supervised biomedical image segmentation
problem are mostly graph-based. By contrast, deep network based semi-supervised
learning methods have not drawn much attention in this field. In this work, we
propose Deep Multi-Planar Co-Training (DMPCT), whose contributions can be
divided into two folds: 1) The deep model is learned in a co-training style
which can mine consensus information from multiple planes like the sagittal,
coronal, and axial planes; 2) Multi-planar fusion is applied to generate more
reliable pseudo-labels, which alleviates the errors occurring in the
pseudo-labels and thus can help to train better segmentation networks.
Experiments are done on our newly collected large dataset with 100 unlabeled
cases as well as 210 labeled cases where 16 anatomical structures are manually
annotated by four radiologists and confirmed by a senior expert. The results
suggest that DMPCT significantly outperforms the fully supervised method by
more than 4% especially when only a small set of annotations is used.

本文提出了一种深度多平面协同训练（DMPCT）模型，以实现半监督医学图像分割问题。实验结果表明，DMPCT 显著优于全监督方法，特别是在使用少量注释时。

基于深度多平面协同训练的半监督多器官分割

Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar  Co-Training

When integrating computational tools such as automatic segmentation into
clinical practice, it is of utmost importance to be able to assess the level of
accuracy on new data, and in particular, to detect when an automatic method
fails. However, this is difficult to achieve due to absence of ground truth.
Segmentation accuracy on clinical data might be different from what is found
through cross-validation because validation data is often used during
incremental method development, which can lead to overfitting and unrealistic
performance expectations. Before deployment, performance is quantified using
different metrics, for which the predicted segmentation is compared to a
reference segmentation, often obtained manually by an expert. But little is
known about the real performance after deployment when a reference is
unavailable. In this paper, we introduce the concept of reverse classification
accuracy (RCA) as a framework for predicting the performance of a segmentation
method on new data. In RCA we take the predicted segmentation from a new image
to train a reverse classifier which is evaluated on a set of reference images
with available ground truth. The hypothesis is that if the predicted
segmentation is of good quality, then the reverse classifier will perform well
on at least some of the reference images. We validate our approach on
multi-organ segmentation with different classifiers and segmentation methods.
Our results indicate that it is indeed possible to predict the quality of
individual segmentations, in the absence of ground truth. Thus, RCA is ideal
for integration into automatic processing pipelines in clinical routine and as
part of large-scale image analysis studies.

本文介绍一种名为 “Reverse Classification Accuracy” 的新型绩效评估方法，可用于新数据上的分割自动化工具的部署与预测。该方法利用预测分割结果训练反向分类器，并通过对一组具有可用标准数据的参考图像进行评估来确定分割工具的性能。我们的定量实验表明该方法对于多器官分割非常适用。